Orbital spectral variables, growth analysis and sugarcane yield

Abstract

Temporal analysis of crop development in commercial fields requires tools for large area monitoring, such as remote sensing. This paper describes the temporal evolution of sugar cane biophysical parameters such as total biomass (BMT), yield (TSS), leaf area index (LAI), and number of plants per linear meter (NPM) correlated to Landsat data. During the 2000 and 2001 cropping seasons, a commercial sugarcane field in Araras, São Paulo state, Brazil, planted with the SP80-1842 sugarcane variety in the 4th and 5th cuts, was monitored using nine Landsat images. Spectral data were correlated with agronomic data, obtained simultaneously to the imagery acquisition. Two methodologies were used to collect spectral data from the images: four pixels (2 × 2) window and average of total pixels in the field. Linear and multiple regression analysis was used to study the spectral behavior of the plants and to correlate with agronomic variables (days after harvest-DAC, LAI, NPM, BMT and TSS). No difference was observed between the methodologies to collect spectral data. The best models to describe the spectral crop development in relation to DAC were the quadratic and cubic models. Ratio vegetation index and normalized difference vegetation index demonstrated correlation with DAC, band 3 (B3) was correlated with LAI, and NDVI was well correlated with TSS and BMT. The best fit curves to estimate TSS and BMT presented r² between 0.68 and 0.97, suggesting good potential in using orbital spectral data to monitor sugarcane fields.

Sugarcane yield estimates using time series analysis of spot vegetation images

“…The yield estimation methods for sugarcane adopted by the Brazilian government are considered subjective because they are based on information gathered from direct inquiries to the production sector, such as field research using questionnaires, surveys on information about demands on agriculture raw materials, use of yield historical data and field observations on plant behavior (IBGE, 2002;CONAB, 2007). The possibility of determining sugarcane development by spectral data such as the Normalized Difference Vegetation Index -NDVI (Simões et al, 2005a) and the correlation between vegetation indices and sugarcane yield (Simões et al, 2005b;Simões et al, 2009) demonstrate the potential of using spectral data for yield estimates at local scale. It is necessary to study this potential in a regional scale, in order to gather timely information about the plant development and about the expected yield before the harvesting.…”

Análise Temporal das Variações de Parâmetros Biofísicos da Cana-de-Açúcar em Jaíba - MG

“…Sendo que o NDVI apresentou maior variação de uma fase para outra em todos os anos safras analisados. Wiegand et al (1991) e Simões et al (2009) apontam que o fato de os dados do comportamento espectral seguirem a evolução temporal de variáveis agronômicas ratificam a potencialidade do sensoriamento remoto na detecção de dados para monitorar as condições de produção de culturas agrícolas.…”

Basic principles of graphing data

“…This does not mean that one should never present vertical tick mark labels at the y-axis, but that in most situations this will not be the best solution. Try to avoid putting text within the data rectangle when it can distract a reader's attention (e.g., Mohapatra and Kariali 2008); for example, regression equations are far too often put inside graphs (Fiorio and Dematte, 2009;Pahlavani et al, 2009;Simoes et al, 2009). However, Tufte's (2006, p. 120) graph is an excellent exception to this rule.…”